LEAF (Liver Tumor dEtection And classiFication AI)

Official title

Clinical Research on the Use of Non-contrast CT Combined With AI for Early Screening for Liver Malignancy

Overview

This study aims to assess the feasibility of leveraging non-contrast CT and artificial intelligence to detect liver cancer in consecutive real-world patients. To this end, we deploy LEAF in a prospective real-world clinical setting for real-time monitoring, with a particular focus on flagging cases with liver cancer that may be missed by routine clinical workflow.

Detailed description

This prospective real-world trial will be conducted at FAHZU, a high-volume tertiary medical center in mainland China.

LEAF will be deployed within the hospital information system through the DAMO Intelligent Medical Imaging interface, allowing it to flag potential liver lesions in real time. Approximately 2500 consecutive patients undergoing non-contrast CT examinations will be enrolled starting in July 2026. All incoming non-contrast chest and abdominal CT scans will be simultaneously reviewed by radiologists in routine clinical workflow and processed by LEAF in real-time. Daily logs of LEAF-positive alerts will be maintained by the research team. A prespecified clinical action committee composed of hepatobiliary surgeons and abdominal radiologists will review the case to assess whether the AI finding warrants communication to the treating physician of these patients. For patients with suspected malignant liver tumors, the committee's consensus on the presence of suspicious lesions will be communicated to their attending physicians, who will then decide whether additional diagnostic assessment is indicated according to standard clinical practice. The standard radiology workflow will not be altered by the study, and LEAF will be evaluated as a risk-stratification and case-flagging tool rather than a replacement for radiologist interpretation.

Interventions

• Device LEAF(Liver tumor dEtection And classiFication AI)
  The LEAF (Liver tumor dEtection And classiFication AI) model will assist in image interpretation. Patients with positive results for liver malignancy while not reported in standard-of-care CT report will be reviewed by a prespecified clinical action committee composed of hepatobiliary surgeons and abdominal radiologists will review the case and decide whether the AI finding warrants communication to the treating physician of these patients. For patients with suspected malignant liver tumors, the

Primary outcome measures

• Detection accuracy in liver tumor assisted by LEAF (Liver tumor dEtection And classiFication AI) [Time frame: Within 4 weeks after enrollment]

Secondary outcome measures (2)

• AI diagnostic performance: patient-level Positive Predictive Value (PPV) and Negative Predictive Value (NPV) of liver malignancy identification [Time frame: Within 4 weeks after enrollment]
• Clinical utility: number of AI-detected and originally overlooked liver malignant lesions [Time frame: Within 4 weeks after enrollment]

Eligibility criteria

Inclusion criteria

Age range 18 years and above;

Underwent non-contrast chest or abdominal CT examination with liver coverage;

Patients with an established diagnosis of cirrhosis;

Patients with an established diagnosis of extrahepatic cancer.

Exclusion criteria

Patients who have been diagnosed with malignant liver tumor;

Patients who underwent liver transplantation;

Low quality image, severe artifacts and noise.

Criteria are shown verbatim from the registry (in English). Final eligibility is always assessed by the study center.

Healthy volunteers: Yes

Study design

Allocation

N/A

Model

Single group

Masking

Open label

Primary purpose

Diagnostic

Study locations

China · 1 center

• the First Affiliated Hospital, School of Medicine, Zhejiang University — Hangzhou

Identifiers

NCT: NCT06859840 · LEAF

Primary sources (government registries)

• ClinicalTrials.gov (US NIH)

View this study on ClinicalTrials.gov ↗